AI has added an unlikely language to its catalog of translation capabilities: brain waves. Joseph Makin led a group from the University of California San Francisco to render a person’s neural signals as English text while the person while the person read a sentence aloud.

AI’s ability to produce synthetic pictures that fool humans into believing they’re real has spurred a race to build neural networks that can tell the difference. Recent research achieved encouraging results.

Which comes first, training a reinforcement learning model or extracting high-quality features? New work avoids this chicken-or-egg dilemma by doing both simultaneously.

Your body disturbs Wi-Fi signals as you move through them. New research takes advantage of the effect to recognize the presence of people. Yang Liu and colleagues at Syracuse University detected people in a room with a Wi-Fi router by analyzing the signal.

Robot vacuum cleaners are pretty good at navigating rooms, but they still get stuck in tight spaces. New work takes a step toward giving them the smarts they’ll need to escape the bathroom.

Given real-world constraints on memory and processing time, images are often downsampled before they’re fed into a neural network. But the process removes fine details, and that degrades accuracy. A new technique squeezes images with less compromise.

Wait a minute — we added training data, and our model’s performance got worse?! New research offers a way to avoid so-called double descent.

Faced with a classification task, an important step is to browse the catalog of machine learning architectures to find a good performer. Researchers are exploring ways to do it automatically.

Batch normalization is a technique that normalizes layer outputs to accelerate neural network training. But new research shows that it has other effects that may be more important.

Last week we reported on a formula to determine model width and dataset size for optimal performance. A new paper contributes equations that optimize some training parameters.

Glass bottles and crystal bowls bend light in strange ways. Image processing networks often struggle to separate the boundaries of transparent objects from the background that shows through them. A new method sees such items more accurately.

Chemists typically develop new antibiotics by testing close chemical relatives of tried-and-true compounds like penicillin. That approach becomes less effective, though, as dangerous bacteria evolve resistance to those very chemical structures. Instead, researchers enlisted neural networks.

How much data do we want? More! How large should the model be? Bigger! How much more and how much bigger? New research estimates the impact of dataset and model sizes on neural network performance.

A simple linear classifier paired with a self-supervised feature extractor outperformed a supervised deep learning model on ImageNet, according to new research.

Which dataset was used to train a given model? A new method makes it possible to see traces of the training corpus in a model’s output.